So sánh phương pháp

	Mô hình Chủ đề NMF	Topic Modeling (Mô hình hóa chủ đề)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	1999	1999–2003
Người khởi xướng	Lee, D. D. & Seung, H. S.	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Loại	Matrix factorization / unsupervised topic model	Unsupervised generative probabilistic model
Công trình gốc	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Tên gọi khác	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Liên quan	4	5
Tóm tắt	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.